Artificial Intelligence Applications in Cybersecurity: Threat Detection, Challenges, Framework and Future Directions

Abstract

The rapid increase in cyber threats has prompted organizations to explore advanced solutions, with artificial intelligence (AI) emerging as a critical tool in cybersecurity. AI applications, including machine learning, deep learning, and hybrid models, provide enhanced threat detection, mitigation, and predictive capabilities. This study aims to systematically review the role of AI in cybersecurity, identify challenges and limitations, and propose emerging strategies for improving organizational resilience. A systematic literature review (SLR) methodology was employed, sourcing peer-reviewed articles, conference proceedings, and reputable journals from databases such as IEEE Xplore, ScienceDirect, Springer, MDPI, and Wiley. Boolean operators and keywords such as “AI,” “cybersecurity,” “threat detection,” “machine learning,” and “blockchain” were used, with inclusion criteria focused on studies addressing AI applications in threat detection and mitigation from 2019 to 2025. Data were extracted and synthesized using thematic analysis, categorizing findings into AI applications, challenges, and future directions. The results indicate that AI significantly enhances threat detection accuracy and operational efficiency, particularly through hybrid AI models, predictive threat intelligence, and blockchain integration. Key challenges include adversarial attacks, model explainability, data quality, and regulatory compliance. In conclusion, AI holds substantial potential to transform cybersecurity, provided technical, operational, and regulatory limitations are addressed. The study proposes a comprehensive AI-driven cybersecurity framework to guide organizations in developing robust, adaptive, and trustworthy security systems.